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DETECTION OF LUNG CANCER USING SOFT COMPUTING TECHNIQUES

Sanjeev Indora, Dr. Dinesh Kr. Atal Kr. Atal, Supiksha Jain

Abstract


Cancer is a generic term that can affect any part of the body. Most cancers, 90%-95%, are because by environmental and lifestyle variables which cause genetic mutations. Inherited genetics account for 5% to 10% of the total. Early identification of lung cancer has improved patient survival and has been a vital study topic. It starts within the cells covering the bronchi and lung parts like bronchioles or alveoli. Due to the shape of cancer cells, early detection of lung cancer is challenging because most of the cells overlap. Imaging machines used to diagnose cancer within the body are X-Ray, CT, MRI, PET, SPECT, and transthoracic fine-needle aspiration. Various researchers have proposed lung cancer prediction models in the last few years, such as Feature fusion mechanism, Reference-model, 3D-CNN, Optical Flow Methods, Cloud-Based 3DDCNN CAD system, and likewise that is used to identify the nodules. The five processes of a lung cancer diagnosis include preprocessing, segmentation, feature extraction, feature selection, and classification. A comprehensive review of earlier research has been outlined in this paper to illustrate different methods and techniques for lung cancer detection, including their benefits and limitations

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References


Yang, Yubin, et al. "A chromatic image understanding system for lung cancer cell identification based on fuzzy knowledge." Innovations in Applied Artificial Intelligence: 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Ottawa, Canada, May 17-20, 2004. [2]Z. H. Zhou, Y. Jiang, Y. Bin Yang, and S. F. Chen, “Lung cancer cell identification based on artificial neural network ensembles,” Artif. Intell. Med., vol. 24, no. 1, pp. 25–36, 2002, doi: 10.1016/S09333657(01)00094-X.

Zenodo - Research. Shared.” https://zenodo.org/.

ResearchGate. “Figure 1. New Cases and Deaths Estimated Worldwide in Both Sexes And...” ResearchGate, 2020. https://www.researchgate.net/figure/New-cases-and-deaths-estimated-worldwide-in-both-sexes-and-all-ages-related-to-cancer_fig1_355885669.

D. H. Hirpara et al., “Severe symptoms persist for Up to one year after diagnosis of stage I-III lung cancer: An analysis of province-wide patient reported outcomes,” Lung Cancer, vol. 142, no. November 2019, pp. 80–89, 2020, doi: 10.1016/j.lungcan.2020.02.014.

Y. Song, W. Cai, Y. Wang, and D. D. Feng, “Location Classification of Lung NODULES WITH OPTIMIZED GRAPH CONSTRUCTION Biomedical and Multimedia Information Technology ( BMIT ) Research Group , School of Information Technologies , University of Sydney , Australia Bradley Department of Electrical and,” Signal Processing, pp. 1439–1442, 2012.

F. Zhang, W. Cai, Y. Song, M. Z. Lee, S. Shan, and D. Dagan, “Overlapping node discovery for improving classification of lung nodules,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 5461–5464, 2013, doi: 10.1109/EMBC.2013.6610785.

S. L. A. Lee, A. Z. Kouzani, and E. J. Hu, “Automated detection of lung nodules in computed tomography images: A review,” Mach. Vis. Appl., vol. 23, no. 1, pp. 151–163, 2012, doi: 10.1007/s00138-010-0271-2.

B. Jehangir, S. R. Nayak, and S. Shandilya, “Lung Cancer Detection using Ensemble of Machine Learning Models,” no. Ml, pp. 411–415, 2022, doi: 10.1109/confluence52989.2022.9734212. [10] T. Van den Wyngaert et al., “The EANM practice guidelines for bone scintigraphy,” Eur. J. Nucl. Med. Mol. Imaging, vol. 43, no. 9, pp. 1723–1738, 2016, doi: 10.1007/s00259-016-3415-4.

Mira, Joaquin G., et al. "Advantages and limitations of computed tomography scans for treatment planning of lung cancer." International Journal of Radiation Oncology* Biology* Physics 8.9 (1982): 1617-1623.

H. Cao et al., “A Two-Stage Convolutional Neural Networks for Lung Nodule Detection,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 7, pp. 2006– 2015, 2020, doi: 10.1109/JBHI.2019.2963720.

A. Seal, D. Bhattacharjee, and M. Nasipuri, “Predictive and probabilistic model for cancer detection using computer tomography images,” Multimed. Tools Appl., vol. 77, no. 3, pp. 3991–4010, 2018, doi: 10.1007/s11042-017-4405-7.

L. Patriquin et al., “Early Detection of Lung Cancer with Meso Tetra (4-Carboxyphenyl) Porphyrin- Labeled Sputum,” J. Thorac. Oncol., vol. 10, no. 9, pp. 1311–1318, 2015, doi: 10.1097/JTO.0000000000000627.

M. V. A.Gajdhane and P. D. L.M, “Detection of Lung Cancer Stages on CT scan Images by Using Various Image Processing Techniques,” IOSR J. Comput. Eng., vol. 16, no. 5, pp. 28–35, 2014, doi: 10.9790/066116532835.

S. C. Park et al., “Computer-aided detection of early interstitial lung diseases using low-dose CT images,” Phys. Med. Biol., vol. 56, no. 4, pp. 1139–1153, 2011, doi: 10.1088/0031-9155/56/4/016.

T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient CAD system for pulmonary nodule detection in CT imagery,” Med. Image Anal., vol. 14, no. 3, pp. 390–406, 2010, doi: 10.1016/j.media.2010.02.004.

Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,” IEEE Trans. Med. Imaging, vol. 20, no. 7, pp. 595–604, 2001, doi: 10.1109/42.932744.

Christopher Thomas, “U-Nets with ResNet Encoders and Cross Connections - towards Data Science,” Medium (Towards Data Science, March 14, 2019), . |

P. Gupta and M. Dixit, “Image-based crack detection approaches : a comprehensive survey Image-based crack detection approaches : a comprehensive survey,” no. May, 2022, doi: 10.1007/s11042-022-13152-z. [21] A. Meldo, L. Utkin, M. Kovalev, and E. Kasimov, “The natural language explanation algorithms for the lung cancer computer-aided diagnosis system,” Artif. Intell. Med., vol. 108, no. April, p. 101952, 2020, doi: 10.1016/j.artmed.2020.101952. [22] F. Liao, M. Liang, Z. Li, X. Hu, and S. Song, “Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 11, pp. 3484–3495, 2019, doi: 10.1109/TNNLS.2019.2892409.

G. Singadkar, A. Mahajan, M. Thakur, and S. Talbar, “Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation,” J. Digit. Imaging, vol. 33, no. 3, pp. 678–684, 2020, doi: 10.1007/s10278-019-00301-4.

Y. R. Baby and V. K. Ramayyan Sumathy, “Kernelbased Bayesian clustering of computed tomography images for lung nodule segmentation,” IET Image Process., vol. 14, no. 5, pp. 890–900, 2020, doi: 10.1049/iet-ipr.2018.5748.

S. Sharma, P. Fulzele, and I. Sreedevi, “Hybrid Model for Lung Nodule Segmentation based on Support Vector Machine and k-Nearest Neighbor,” Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, pp. 170–175, 2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00034.

B. K. J. Veronica, “An effective neural network model for lung nodule detection in CT images with optimal fuzzy model,” Multimed. Tools Appl., vol. 79, no. 19– 20, pp. 14291–14311, 2020, doi: 10.1007/s11042-02008618-x.

W. Li, P. Cao, D. Zhao, and J. Wang, “Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images,” Comput. Math. Methods Med., vol. 2016, 2016, doi: 10.1155/2016/6215085.




DOI: https://doi.org/10.37591/rrjoi.v13i2.3221

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